Person:

Ranjan, Nitin

India’s return to caste enumeration after a 94-year hiatus is not merely a statistical event but a watershed in the nation’s unfinished struggle for social justice. This paper interrogates the consequences of governing with century-old caste data, exposing how this “data desert” perpetuates the invisibility of OBCs, Denotified Tribes, Dalit sub-castes, and intersectional minorities. Drawing on the frameworks of data justice, intersectionality, and the political economy of enumeration, we analyze empirical evidence from the SECC 2011, Bihar’s 2023 caste survey, and state and central reports. The findings reveal systemic upper-caste overrepresentation, elite capture within OBC reservations, and the ongoing erasure of Denotified Tribes and Dalit women and trans persons. We argue that blanket quotas without granular sub-categorization have hollowed out the transformative promise of affirmative action. This is the empirical foundation—and the policy roadmap—to dismantle caste-based privilege and forge a truly just republic.Only by reckoning with its data deficit can India renew its constitutional contract and advance genuine social equity.

India’s decision to reintroduce caste enumeration after nearly a century marks a pivotal moment in the country’s democratic journey. While often framed as a technical or administrative challenge, this paper argues that caste data collection is inherently political—shaped by power asymmetries that determine who is counted, how categories are constructed, and whose claims are rendered legible to the state. Drawing on Archon Fung’s “four faces of power” framework—constitutive, agenda-setting, deliberative, and operational—this paper critically examines how exclusion operates at every stage of India’s data architecture. Through case studies of the 2011 Socio-Economic and Caste Census (SECC), state-level surveys in Bihar and Karnataka, and judicial rulings on caste-based reservations, the paper reveals how dominant groups use technical ambiguity, institutional opacity, and selective data release to maintain structural advantage.

Complementing this power analysis with theories of data justice and intersectionality, the paper underscores how even when data is collected, it often erases or misrepresents multiply marginalized groups such as Dalit women, Denotified Tribes, and transgender persons. By combining normative critique with empirical analysis, the paper makes the case that caste enumeration is not a divisive act but a democratic necessity—one that can shift India from symbolic recognition to structural redress. The act of counting, this paper concludes, is not merely statistical; it is a form of political reckoning.

This thesis investigates the role of Artificial Intelligence (AI) as a catalyst for the climate transition, analyzing how AI enables emissions reduction, resource optimization, and advanced climate-risk measurement across energy, mobility, agriculture, finance, and urban systems. With foundational models increasingly commoditized, competitive advantage shifts toward high-quality data, integration capabilities, and alignment with emerging ESG and disclosure standards. The study proposes a structured framework for evaluating and deploying Climate AI solutions, emphasizing measurable climate impact, policy-fit, and system-level scalability. Case studies illustrate how AI-enabled platforms—particularly in carbon accounting, smart grids, predictive industrial systems, and nature monitoring—can accelerate climate outcomes when supported by coordinated stakeholder action. The thesis concludes that AI is evolving into essential climate infrastructure, requiring investment strategies that merge venture-style innovation with long-horizon sustainability priorities.

This report applies behavioural science to analyse the persistent decline in routine childhood immunization in Mozambique. Using Ipsos household data, latent class analysis, and gender-dynamics mapping, it identifies socio-cultural, cognitive, and informational barriers that limit vaccine uptake. A review of 58 global interventions informs a set of behaviourally grounded recommendations—spanning community-led communication, mHealth reminders, social-norm messaging, and improved health-worker interaction—to address demand-side frictions. The report offers a scalable framework for strengthening routine immunization in low-resource settings.